However, directly porting ML models designed for high-end edge devices such as smartphones or single-board computers are not suitable for microcontrollers.
A typical microcontroller has 128 KB RAM and 1 MB of flash, while a smartphone can have 4 GB of RAM and 64 GB of storage [1]}.
The ultra resource limitations of microcontroller class IoT nodes demand the design of a systematic workflow and tools to guide on-board implementations of ML pipelines.
